In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning
Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, Nasem Badreldin
Abstract
Assessing nitrogen (N) status in potato (Solanum tuberosum L.) during the growing season is crucial for optimizing fertilizer application, aligning it with crop demand, and improving N use efficiency, particularly in Western Canada, where extensive potato cultivation supports the agricultural industry. This study evaluated the performance of three machine learning (ML) models—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR)—for predicting potato N status and examined the impact of feature selection techniques, including Partial Least Squares Regression (PLSR), Boruta, and Recursive Feature Elimination (RFE). A field experiment was conducted in 2023 and 2024 near Carberry, Manitoba, Canada, with plots receiving different N rates from various fertilizer sources. Multispectral drone imagery was collected throughout the growing seasons, and key vegetation indices (VIs) related to plant N concentration were extracted for model training. Among the VIs, Cl green exhibited the highest correlation with petiole NO3-N concentration (PNC). The results indicate that RF outperformed SVM and GBR, achieving the highest coefficient of determination (R2 = 0.571) and the lowest mean absolute error (MAE = 0.365%) using the RFE feature selection method. Feature selection enhanced model performance in specific cases, notably RF with RFE, and both SVM and GBR with Boruta. These findings highlight the potential of ML-based approaches for in-season potato N monitoring and emphasize the importance of feature selection in enhancing predictive accuracy.